## Variance-Covariance Matrix

1. The problem statement, all variables and given/known data

Let $$\Sigma =$$
( var(X1) cov(X1, X2) )
( cov (X2. X1) var(X2) )

Show that $$Var (a_1 X_1 + a_2 X_2) = a^T \Sigma a$$

where $$a^T = [a_1 a_2]$$ is the transpose of the of the column vector a

2. Relevant equations

3. The attempt at a solution

I got this far:

$$Var (a_1 X_1 + a_2 X_2) = a_1^2 Var(X_1) + a_2^2 Var(X_2) + 2a_1 a_2 Cov (X_1, X_2) = a_1^2 Var(X_1) + a_2^2 Var(X_2) + a_1 a_2 Cov (X_1, X_2) + a_1 a_2 Cov (X_2, X_1)$$

Thats all I got so far, any hints
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 Aren't you done? Isn't that what $$a^T \Sigma a$$ is?
 Thought there was more to it than that. There's another part of the question that says: Using $$Var (a_1 X_1 + a_2 X_2)$$ show that for every choice of a1 and a2 that $$a^T \Sigma a \geq 0$$ Can I assume that $$\Sigma$$ is always positive?

## Variance-Covariance Matrix

$$Var (a_1 X_1 + a_2 X_2)\ge0$$ always, since it's variance! And you just showed it equals $$a^T \Sigma a$$

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